• C++ API Documentation

  • Quickstart
  • Matrices and data
  • Loading and saving
  • Citation
  • Core functionality

    • Core math utilities
    • Distances
    • Distributions
    • Kernels
  • Trees

    • KDTree
    • MeanSplitKDTree
    • BallTree
    • MeanSplitBallTree
    • VPTree
    • RPTree
    • MaxRPTree
    • UBTree
    • BinarySpaceTree
  • Classification

    • AdaBoost
    • DecisionTree
    • HoeffdingTree
    • LinearSVM
    • LogisticRegression
    • NaiveBayesClassifier
    • Perceptron
    • RandomForest
    • SoftmaxRegression
  • Regression

    • BayesianLinearRegression
    • DecisionTreeRegressor
    • LARS
    • LinearRegression
  • Clustering

    • MeanShift
  • Geometry
  • Preprocessing

    • Normalizing labels
    • Dataset splitting
  • Transformations

    • AMF
    • LocalCoordinateCoding
    • LMNN
    • NCA
    • NMF
    • PCA
    • RADICAL
    • SparseCoding
  • Modeling

    • Cross-validation
    • Hyperparameter tuning
  • Embedded Applications

    • cross-compile and run k-NN on a Raspberry Pi 2 (armv7)
    • Setting up an mlpack cross-compilation environment
  • Examples
  • Developers

• Binding API

  • Python quickstart
  • Python binding documentation
  • Julia quickstart
  • Julia binding documentation
  • R quickstart
  • R binding documentation
  • CLI quickstart
  • CLI binding documentation
  • Go quickstart
  • Go binding documentation

• mlpack and Google Summer of Code [top]
  • How to be a good candidate
  • Things to be familiar with

mlpack and Google Summer of Code

mlpack is a proud participant in Google Summer of Code. We have been a part of the program since 2013, with over 50 students accepted over the years for mlpack (all of whom succeeded in the program). Each year, we receive very many applications and it is a competitive process, so this page exists to help you determine if you could be a strong candidate.

🔗 How to be a good candidate

The two most important qualities that an mlpack GSoC candidate can possess is the ability to be self-sufficient and the willingness to learn.

Self-sufficiency is key because mentors have limited time and can’t put in as much time helping the student as the student is putting in. However, this of course does not mean that a student (or a prospective student) can never ask any questions! mlpack is a complex library and can sometimes take help and explanation to understand.

A willingness to learn is also important because virtually every potential project will require the student to become familiar with a new algorithm or C++ technique. mlpack is a complex library with many components and it is likely during your project that you might have to use or interact with some other part of the codebase.

🔗 Things to be familiar with

Of course, these are not the only important ingredients for a successful GSoC project. A student should ideally be familiar with

• open source software development: opening pull requests, using git, opening issues. mlpack uses Github, which has great documentation. You can learn about the workflow using this link, if you are not already familiar.

• using the development toolchain on your computer: you should be able to download and compile mlpack, make changes to the code, and recompile with the new changes. There is a section in the README for how to build mlpack and would be a great place to get started. If you’re on Windows, then the Windows build guide could be very useful. See also the Community page for more information on getting started.

• at least intermediate C++ knowledge: mlpack uses lots of different C++ paradigms including template metaprogramming, C++ features like rvalue references, and other strategies, in order to make the code fast. You should be at least familiar with some of these language features and what templates are, even if you have not used them in-depth, so that you can understand the mlpack codebase. Some examples of patterns that are often used inside of mlpack are SFINAE (example in mlpack, see std::enable_if usages), policy-based design, and compile-time class traits. Here are some other useful resources for learning template metaprogramming, and some useful reference books. If some of this sounds new to you, don’t feel overwhelmed; it’s not a necessity, but it is helpful. You should at least be prepared to learn about it!

• project-specific knowledge: you’ll need to have an in-depth understanding of the specific project that you choose. If you’re not sure what project you want to work on, see the SummerOfCodeIdeas wiki page, which has ideas for GSoC projects that you might find interesting. But you aren’t required to do one of those projects; if you have another interesting idea, propose it and see if a mentor is interested in supervising the project! If you have any questions about a project, be aware that it’s possible that the question has already been answered on Github. Take a look through the closed issues or search to see if there’s already an answer to your question. Get involved!

For a strong proposal, it’s important to be a part of the mlpack community, via contributions, code reviews, helping others solve their issues, and so forth. There are some pointers on the community page on how to start contributing and get involved.

When it comes time to write your proposal, we do have a proposal guide that you can take a look at to guide your application.